vera-data-survival-reviewing

name	vera-data-survival-reviewing
description	Runs survival diagnostics and primary hypothesis tests for right-censored time-to-event outcomes. Produces Kaplan-Meier curves with number-at-risk tables, log-rank test for group comparison, median survival, landmark survival rates, and HR preview from univariate Cox regression. Outputs .R and .py scripts with publication-quality plots. Triggered when user has a time-to-event outcome and says "survival outcome," "time to event," "Kaplan-Meier," "hazard," "OS/PFS/DFS." Right-censored data only. Competing risks, recurrent events, and multi-state models are out of scope. Does not handle binary, count, continuous, ordinal, repeated measures, or SEM outcomes.
allowed-tools	Read, Bash, Write, Edit

Survival Outcome — Diagnostics & Hypothesis Testing

Table of Contents

• Scope Boundary
• Workflow
• Decision Tree
• Required Inputs
• Code Structure
• Reporting Standards
• Hypothesis Tests
• Example Dataset
• Method Status
• Minimal Smoke Test
• Cross-Skill Interface

Open-source skill.

Scope Boundary

Use this skill when:

• The endpoint is standard right-censored time-to-event data and the first need is KM plus log-rank style inference.
• A univariate or simple grouped survival baseline is appropriate before multivariable hazard modeling.

Do not use this skill when:

• The data involve left/interval censoring, competing risks, or multi-state structures.
• The primary question already depends on recurrent-event or time-varying-covariate modeling.

Workflow

Read each step file in workflow/ before executing that step.

Step	Responsibility	Executor	Document	Input	Output
Collect	Collect Inputs	Main Agent	workflow/step01-collect-inputs.md	User input	Structured input summary
Diagnose	Check Distribution	Main Agent	workflow/step02-check-distribution.md	Prior step output	PART 1 code block
Test	Run Primary Test	Main Agent	workflow/step03-run-primary-test.md	Prior step output	PART 2-3 code blocks

Decision Tree

1. CHECK FOLLOW-UP & CENSORING
   ├── Censoring rate > 80% → Warning: limited events, wide CIs expected
   ├── Censoring rate < 5% → Note: standard regression may suffice
   └── Otherwise → proceed normally

2. GROUP COMPARISON
   ├── 2 groups → Log-rank test + HR from univariate Cox
   └── 3+ groups → Log-rank test + pairwise log-rank (Bonferroni)

Required Inputs

Role	What to collect
Time variable	Continuous, ≥0, follow-up/survival time
Event indicator	Which value = event occurred, which = censored
Group variable	What defines groups, how many levels
Predictors	For recommendation block (not executed)

Code Structure

PART 0: Setup & Data Loading
PART 1: Follow-Up & Censoring Diagnostics → plot_01_km_overall.png, plot_01b_event_histogram.png
PART 2: Primary Hypothesis Test           → plot_02_km_groups.png
PART 3: Recommendation Block              → text listing additional analyses available

Reporting Standards

1. p-values: "< .001" not "0.000"; exact to 3 decimals otherwise
2. Hazard ratio: HR with 95% CI, "HR = X.XX, 95% CI [X.XX, X.XX]"
3. Median survival: always with 95% CI
4. Survival rates at landmarks: with 95% CI
5. Censoring: always report % censored overall and by group
6. Log-rank: chi-sq(df) = X.XX, p = .XXX
7. Decimal places: 2 for median survival, 3 for p and HR
8. Non-significance: "not statistically significant at alpha = .05" — never "no effect"
9. Cox regression: when reported, include Schoenfeld-residual test for proportional hazards (global p-value + per-covariate). Flag any PH violation explicitly.

Hypothesis Tests

Scenario	Test
2 independent groups	Log-rank + univariate Cox HR
3+ independent groups	Log-rank + pairwise log-rank (Bonferroni)

Paired/clustered designs are out of scope for this version.

Example Dataset

R built-in survival::lung: outcome = time (survival time), status (1=censored, 2=dead). Predictors: age, sex, ph.ecog, ph.karno, pat.karno, meal.cal, wt.loss. Python: from lifelines.datasets import load_lung or reconstruct from R.

Method Status

Status	Methods
Implemented in this skill	Kaplan-Meier summaries, log-rank tests, median survival, landmark survival rates, univariate Cox preview
Implemented downstream in vera-data-survival-generating	Multivariable Cox, AFT, recurrent-event models, random survival forest, boosting survival methods
Out of scope in this open-source baseline	Competing-risks, multi-state, left-censored, and interval-censored survival families

Minimal Smoke Test

• Smoke-test prompt: "Run vera-data-survival-reviewing on survival::lung, using time as follow-up, status as the event indicator, and sex as the grouping variable. Produce the standard baseline artifacts."

Cross-Skill Interface

Output:
├── code_r      → .R script
├── code_python → .py script
├── figures/    → 2 PNGs (KM overall + KM groups)
└── recommendations → text block (additional analyses available)

Next step: Invoke vera-data-survival-generating from this skillset to run the full pipeline (additional tests, subgroup analysis, modeling, manuscript generation). See ../../CROSS-SKILL-INTERFACE.md for the shared handoff contract.